AlexeyAB
commented
6 years ago If the dataset includes people marked quite far away in equal quality to the people marked in close will the object detection work ?
What do you mean by "equal quality"?
If this is not a dataset issue then why such scaling algorithms are producing better results ? https://github.com/peiyunh/tiny
Better than what? I don't know what AP can we get by training Yolo v3 on (FDDB and WIDER FACE).
As I see they used Resnet-101 as backbone, and detector at 3 scales. Yolo v3 used Feature Pyramids network (with residual connections) as backbone, and detector at 3 scales.
I scrolled through the article, but I did not find what value of subsampling they used for each of the three detectors, but as I see they used 3 resolution scales: https://github.com/peiyunh/tiny/blob/745a49cb38182a79f17d5090257914669d8e8089/tiny_face_detector.m#L85-L86
Are we missing few image scaling functions in the training ?
What do you mean?
In the Yolo v3, optimal size of object (and optimal anchors) for:
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yolo Region 82 - 32x32 pixels - ~886x886 pixels https://github.com/AlexeyAB/darknet/blob/4403e71b330b42d3cda1e0721fb645cf41bac14f/cfg/yolov3.cfg#L607
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yolo Region 94 - 16x16 pixels - ~518x518 pixels (up to ~886x886 pixels ) https://github.com/AlexeyAB/darknet/blob/4403e71b330b42d3cda1e0721fb645cf41bac14f/cfg/yolov3.cfg#L693
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yolo Region 106 - 8x8 pixels - ~206x206 pixels (up to ~886x886 pixels ) https://github.com/AlexeyAB/darknet/blob/4403e71b330b42d3cda1e0721fb645cf41bac14f/cfg/yolov3.cfg#L780
You can change
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this line: https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 to this:
layers = -1, 4and this line: https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 to this:
stride=8so yolo Region 106 will detect - 2x2 pixels - ~20x20 pixels (up to ~886x886 pixels )
and
- yolo Region 94 - 16x16 pixels - ~518x518 pixels (up to ~886x886 pixels )
- yolo Region 82 - 32x32 pixels - ~886x886 pixels
Then set width=1024 height=1024 in cfg-file, set random=1 for each of [yolo]-layers.
Change this line: https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/src/detector.c#L132
to float random_val = rand_scale(2); or float random_val = rand_scale(4);
And train about 1 000 000 iterations on (FDDB and WIDER FACE) with only 1 class (face).
Then you can try to get AP for face by using ./darknet detector map ...
deimsdeutsch
dexception
Since Yolov3 has been trained MSCOCO and most of the people marked in the dataset are either close or visible to naked eye. If the dataset includes people marked quite far away in equal quality to the people marked in close will the object detection work ?
If this is not a dataset issue then why such scaling algorithms are producing better results ? https://github.com/peiyunh/tiny
Are we missing few image scaling functions in the training ?